library(tidyverse)
library(targets)
library(kableExtra)targets::tar_load("fit_sp_0_summary_simple")
targets::tar_load("fit_sp_1_summary_model")
targets::tar_load("fit_sp_11_summary_model")
targets::tar_load("fit_sp_2_summary_model")
targets::tar_load("fit_sp_3_summary_model")
targets::tar_load("fit_sp_33_summary_model")
targets::tar_load("fit_sp_4_summary_sma")
targets::tar_load("fit_sp_5_summary_sma")
targets::tar_load("fit_sp_6_summary_sma_err")
targets::tar_load("fit_sp_0_diagnostics_simple")
targets::tar_load("fit_sp_1_diagnostics_model")
targets::tar_load("fit_sp_11_diagnostics_model")
targets::tar_load("fit_sp_2_diagnostics_model")
targets::tar_load("fit_sp_3_diagnostics_model")
targets::tar_load("fit_sp_33_diagnostics_model")
targets::tar_load("fit_sp_4_diagnostics_sma")
targets::tar_load("fit_sp_5_diagnostics_sma")
targets::tar_load("fit_sp_6_diagnostics_sma_err")fit_sp_0_summary_simple |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_1_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_11_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_2_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_3_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_33_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_4_summary_sma |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_5_summary_sma |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_6_summary_sma_err |> filter(rhat > 1.1)
#> # A tibble: 2,094 × 11
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ 1.20e+3 1.20e+3 2.75e+1 2.98e+1 1.15e+3 1.24e+3 1.53 7.29
#> 2 beta[1] 1.73e+0 2.31e+0 4.42e+0 3.85e+0 -6.52e+0 8.83e+0 2.49 4.84
#> 3 beta[2] 2.98e-1 2.98e-1 2.92e-4 3.06e-4 2.98e-1 2.99e-1 1.28 11.3
#> 4 beta[3] 1.19e-4 1.76e-4 2.97e-4 2.26e-4 -5.57e-4 4.88e-4 1.53 7.43
#> 5 beta[4] 3.57e-1 3.57e-1 2.34e-4 2.49e-4 3.57e-1 3.58e-1 1.37 8.90
#> 6 gamma[1] -5.07e+0 -5.09e+0 8.18e-2 7.43e-2 -5.18e+0 -4.90e+0 1.86 5.76
#> 7 gamma[2] -5.30e-1 -5.08e-1 1.34e-1 9.97e-2 -7.82e-1 -3.12e-1 1.84 5.82
#> 8 gamma[3] -2.67e-1 -2.75e-1 8.75e-2 9.11e-2 -3.98e-1 -1.16e-1 1.44 8.06
#> 9 gamma[4] 3.51e-1 3.43e-1 8.79e-2 8.05e-2 2.18e-1 5.14e-1 1.60 6.76
#> 10 sigma_x_u… 3.99e-4 3.48e-4 1.53e-4 1.94e-4 2.05e-4 6.33e-4 2.00 5.45
#> # … with 2,084 more rows, and 2 more variables: ess_tail <dbl>,
#> # .join_data <dbl>It is difficult to model heteroskedasticity and measurement errors at the same time.
div_check(fit_sp_0_diagnostics_simple)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_1_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_11_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_2_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_3_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_33_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_4_diagnostics_sma)
#> [1] "131 of 8000 iterations ended with a divergence 1.6375 %"
div_check(fit_sp_5_diagnostics_sma)
#> [1] "131 of 8000 iterations ended with a divergence 1.6375 %"
div_check(fit_sp_6_diagnostics_sma_err)
#> [1] "6 of 8000 iterations ended with a divergence 0.075 %"targets::tar_load(loo_model)
loo::loo_compare(loo_model[[1]], loo_model[[2]], loo_model[[3]],
loo_model[[4]], loo_model[[5]], loo_model[[6]])
#> elpd_diff se_diff
#> model4 0.0 0.0
#> model1 0.0 0.5
#> model6 -6.4 7.3
#> model5 -7.0 7.4
#> model2 -9.5 5.6
#> model3 -10.8 5.8tar_read(cv_sp)
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.10016tar_load(coef_sp_png)tar_load(coef_sp_png2)tar_load(coef_sp_png3)tar_load(sma_sp_tab)
sma_sp_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Thick~Large | 0.93 [0.85, 1.03] | 0.19 [0.01, 0.37] | 0.86 |
| Thick~Small | 1.02 [0.97, 1.07] | 0 [-0.11, 0.11] | 0.93 |
| Thin~Large | 0.91 [0.82, 1.02] | 0.2 [0.02, 0.37] | 0.71 |
| Thin~Small | 0.93 [0.83, 1.03] | 0.16 [-0.02, 0.35] | 0.84 |
tar_load(sma_sp_ld_tab)
sma_sp_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Dense ~ Thick~Large | 0.96 [0.82, 1.13] | 0.14 [-0.15, 0.43] | 0.79 |
| Dense ~ Thick~Small | 1.02 [0.94, 1.11] | -0.01 [-0.17, 0.15] | 0.90 |
| Dense ~ Thin~Large | 0.8 [0.66, 0.98] | 0.37 [0.09, 0.64] | 0.60 |
| Dense ~ Thin~Small | 0.86 [0.72, 1.03] | 0.27 [-0.01, 0.55] | 0.81 |
| Nondense ~ Thick~Large | 0.75 [0.59, 0.94] | 0.58 [0.22, 0.94] | 0.72 |
| Nondense ~ Thick~Small | 0.99 [0.87, 1.13] | 0.05 [-0.23, 0.32] | 0.82 |
| Nondense ~ Thin~Large | 0.77 [0.65, 0.91] | 0.49 [0.25, 0.74] | 0.56 |
| Nondense ~ Thin~Small | 0.87 [0.71, 1.06] | 0.28 [-0.06, 0.62] | 0.68 |
tar_load(sma_tree_tab)
sma_tree_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| Thick~Large | 0.97 [0.9, 1.04] | 0.12 [-0.03, 0.26] | 0.61 |
| Thick~Small | 0.94 [0.9, 0.98] | 0.16 [0.07, 0.24] | 0.79 |
| Thin~Large | 0.91 [0.84, 0.97] | 0.21 [0.09, 0.32] | 0.45 |
| Thin~Small | 0.91 [0.85, 0.98] | 0.19 [0.07, 0.31] | 0.66 |
tar_load(sma_tree_ld_tab)
sma_tree_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| Dense ~ Thick~Large | 0.89 [0.79, 1] | 0.25 [0.06, 0.44] | 0.49 |
| Dense ~ Thick~Small | 0.91 [0.85, 0.97] | 0.21 [0.1, 0.32] | 0.76 |
| Dense ~ Thin~Large | 0.75 [0.67, 0.85] | 0.44 [0.29, 0.6] | 0.34 |
| Dense ~ Thin~Small | 0.8 [0.72, 0.9] | 0.36 [0.2, 0.52] | 0.65 |
| Nondense ~ Thick~Large | 0.74 [0.64, 0.86] | 0.61 [0.38, 0.83] | 0.43 |
| Nondense ~ Thick~Small | 0.84 [0.78, 0.91] | 0.38 [0.24, 0.53] | 0.68 |
| Nondense ~ Thin~Large | 0.73 [0.65, 0.81] | 0.57 [0.42, 0.72] | 0.28 |
| Nondense ~ Thin~Small | 0.89 [0.78, 1.01] | 0.25 [0.03, 0.46] | 0.38 |
tar_load(cv_sp)
cv_sp
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.10016tar_load(cv_tree)
cv_tree
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.718 0.0421
#> 2 fit 2 0.684 0.0467
#> 3 fit 3 0.702 0.0440
#> 4 fit 4 0.739 0.0385
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.8911 0.8176
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.05804 0.03071 0.05749
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.30044 0.16541 0.03573 0.11701
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 1.54932 0.72303 0.01379 0.21886devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os Ubuntu 20.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2022-05-03
#> pandoc 2.16.2 @ /usr/bin/ (via rmarkdown)
#>
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